U.S. Geological Survey, Water Mission Area, Earth Systems Processes Division-Water Cycle Branch, 12201 Sunrise Valley Drive, Mail Stop 431, Reston, VA, 20192, USA.
Pacific Northwest National Laboratory, Earth Systems Science Division, 902 Battelle Boulevard, PO Box 902, MSIN K9-69, Richland, WA, 99352, USA.
Ground Water. 2022 Nov;60(6):721-746. doi: 10.1111/gwat.13206. Epub 2022 May 24.
A state-space model (SSM) of infiltration estimates daily groundwater recharge using time-series of groundwater-level altitude and meteorological inputs (liquid precipitation, snowmelt, and evapotranspiration). The model includes diffuse and preferential flow through the unsaturated zone, where preferential flow is a function of liquid precipitation and snowmelt rates and a threshold rate, above which there is direct recharge to the water table. Model parameters are estimated over seasonal periods and the SSM is coupled with the Kalman Filter (KF) to assimilate recent observations (hydraulic head) and meteorological inputs into recharge estimates. The approach can take advantage of real-time hydrologic and meteorological data to deliver real-time recharge estimates. The model is demonstrated on daily observations from two bedrock wells in carbonate aquifers of northwestern New York (USA) between 2013 and 2018. Meteorological inputs for liquid precipitation and snowmelt are compiled from SNODAS (2021). Results for recharge during winter and spring seasons show preferential flow events to the water table from liquid precipitation, snowmelt, or a combination of the two. Recharge estimates summed annually are consistent with previous estimates of recharge reported from groundwater flow and surface-process models. Results from the SSM and KF point to errors in meteorological inputs, such as the snowmelt rate, that are not compatible with hydraulic head observations. Whereas liquid and solid precipitation are measured at discrete stations and extrapolated to 1-km grid cells, snowmelt is a meteorological modeled outcome that may not represent conditions in the vicinity of monitoring well locations.
一个状态空间模型（SSM）使用时间序列的地下水水位和气象输入（液态降水、融雪和蒸散）来估计地下水补给。该模型包括非饱和带的弥散和优先流，其中优先流是液态降水和融雪率以及一个阈值率的函数，超过该阈值率就有直接补给到地下水位。模型参数在季节性期间进行估计，SSM 与卡尔曼滤波器（KF）耦合，以将最近的观测（水力头）和气象输入纳入补给估计。该方法可以利用实时水文和气象数据提供实时补给估计。该模型在 2013 年至 2018 年期间，在美国纽约西北部碳酸盐含水层的两个基岩井的每日观测中进行了演示。液态降水和融雪的气象输入是从 SNODAS（2021 年）汇编的。冬季和春季补给的结果表明，液态降水、融雪或两者的组合会有优先流事件补给地下水位。每年累计的补给量与地下水流动和地表过程模型报告的先前补给估计值一致。SSM 和 KF 的结果表明，气象输入存在误差，例如融雪率，与水力头观测不匹配。虽然液态和固态降水是在离散站测量并外推到 1 公里网格单元，但融雪是一种气象模型化的结果，可能无法代表监测井位置附近的条件。
